This Mentored Scientist Career Development Award (K01) will provide the specific training, mentorship, and protected time required to facilitate the candidate's transition to research independence. Her training objectives are to develop advanced skills in geographic information systems (GIS) and sociometric network analysis, receive further training in the ethical conduct of research, particularly in international/cross-cultural settings and acquire skills in grant-writing and collaborative research. These training aims will be accomplished through a combination of didactic coursework and specialized workshops, instruction in the responsible conduct of research, directed readings with mentors, and meetings with training committee members who are nationally and internationally renowned experts in HIV, substance abuse, border health, GIS, and social networks research. The research aims fit well with NIDA's mission to bring the power of science to bear on drug abuse and addiction and will be carried out through a study partially embedded in an existing study, El Cuete (R37 DA019829; PI: Strathdee). The specific aims of the proposed research are to: 1) develop GIS models to examine the relationship between neighborhood (colonia) characteristics and clustering of a) HIV and b) recent voluntary drug treatment entry, 2) identify sociometric network attributes associated with a) HIV and b) recent voluntary drug treatment entry and 3) build a multi-level geographically weighted model to examine the independent and combined influence of colonia and sociometric network correlates on a) HIV and b) recent voluntary drug treatment entry. Sixteen seeds from 8 different colonias in Tijuana, Mexico will be selected from those recently enrolled in El Cuete to initiate the peer recruitment of 300 additional IDUs for the K01 study. To promote geographic diversity in the K01 sample, seeds will be required to report greater mobility (live, inject, and/or buy drugs in >1 colonia). To facilitate peer recruitmnt and the identification of all network ties (a sociometric network), participants will receive 10 recruitment tickets (with a unique recruiter ID, distinct from his/her study ID) and more if needed to distribute to IDU network members. IDU network members may be accompanied to the study site by their recruiter (peer-referral); if they arrive alone, they must correctly identify their per recruiter. Participants will also provide information about IDU network members which will be compared with locator information provided by study participants (in El Cuete and the K01 study) to identify additional network ties. Sociometric network data will be created by 1) connecting peer referrals, 2) linking individuals via recruitment ticket IDs, and 3) matching IDU network member information with that on participant locator forms to identify additional ties. Participants will also provide geocodable points for the locations where he/she spends > 1/2 of his/her time, buys drugs, uses drugs, and socializes. Findings from this proposal will have implications for recruiting hidden populations and will guide the development of future multi-level interventions to reduce HIV transmission and increase voluntary drug treatment entry. PUBLIC HEALTH RELEVANCE: Evidence suggests that 1) both neighborhood- and network-level characteristics influence the spread of diseases and risk/health behaviors and that 2) both peer-driven interventions (PDIs) which capitalize on existing social connections and multi-level interventions which include both individual and structural components, are more successful in reducing disease transmission and altering risk/health-seeking behaviors than traditional interventions which focus on the individual. This proposal aims to better understand how these factors act independently and together to influence behaviors and disease transmission in the absence of an intervention; the findings from this proposal can be used to guide the development of multi-level interventions that integrate both structural and network approaches to specifically target the needs of those most at risk. Receiving training in GIS and sociometric network analysis will also improve the candidate's prospects for developing strong research collaborations, build a bridge between risk factor epidemiology and intervention research, and facilitate her transition to an independent investigator.